Fibromyalgia impact and depressive symptoms: Can perceiving a silver lining make a difference?

Individuals with fibromyalgia are at greater risk for depressive symptoms than the general population, and this may be partially attributable to physical symptoms that impair day-to-day functioning. However, individual-level protective characteristics may buffer risk for psychopathology. For instance, the ability to perceive a “silver lining” in one’s illness may be related to better mental and physical health. We examined perceived silver lining as a potential moderator of the relation between fibromyalgia impact and depressive symptoms. Our sample of persons with fibromyalgia (N = 401) completed self-report measures including the Fibromyalgia Impact Questionnaire-Revised, Depression Anxiety Stress Scales, and the Silver Lining Questionnaire. Moderation analyses covaried age, sex, and ethnicity. Supporting hypotheses, increasing impact of disease was related to greater depressive symptoms, and perceptions of a silver lining attenuated that association. Despite the linkage between impairment and depressive symptoms, identifying positive aspects or outcomes of illness may reduce risk for psychopathology. Therapeutically promoting perception of a silver lining, perhaps via signature strengths exercises or a blessings journal, and encouraging cognitive reframing of the illness experience, perhaps via Motivational Interviewing or Cognitive Behavioral Therapy, may reduce depressive symptoms in persons with fibromyalgia.     

A dyadic personality perspective on the Michelangelo phenomenon: How personality traits relate to people’s ideal selves and their personal growth in romantic relationships

Personality matters for romantic relationships. In this study, we investigated personal growth in couples (the Michelangelo phenomenon) and targeted questions of personality effects. We explored whether traits intrapersonally predict ideal selves as well as whether traits intra- and interpersonally account for why some people are more likely to benefit from the Michelangelo phenomenon than others. We used data from a 4-year study of 163 couples (M_age = 50.72 years). Logistic regressions indicate complementarity effects for men, in that those high in neuroticism were likely to wish to be emotionally stable. Actor–partner interdependence models revealed positive actor effects of emotional stability, extraversion, and agreeableness, while few partner effects emerged. We discuss dyadic personal growth in view of individual trait differences.     

Neuroticism as the intensity,reactivity, and variability in day-to-day affect

Neuroticism has been linked to typical levels of affect, affect reactivity to negative events, and variability in affect over time. However, the intercorrelations among these characteristics make it unclear whether neuroticism reflects unique variance in each of these aspects of emotional life. Data from two daily-diary samples revealed that neuroticism was associated with average levels and variability of positive and negative affect and reactivity of negative affect to stressors, but was only uniquely related to mean levels of positive and negative affect. Findings highlight the substantial overlap in affect indices, suggesting that mean levels of affect, at the very least, are at the core of neuroticism, and reveal the need for further research using more nuanced approaches.     

Integrating self-report and performance-based assessment of adaptability in a university context

This research examined different conceptualisations and measurements of adaptability in a university context. We compared two approaches to adaptability: (1) personal attribute and (2) performance construct. In the extant literature, the former is dominated by self-report measures, whereas the latter is dominated by performance-based tasks. In two studies, we developed and tested the factorial structure of self-report adaptability measures and the factorial structure of performance-based tasks. Self-report measures of adaptability converged into two factors: adaptability and inflexibility. Performance-based metrics of adaptability did not converge, however. Self-reported and performance measures were unrelated, calling for a multitrait-multimethod approach to further evaluate construct validity of adaptability measurement. Self-reported adaptability was related to Big-5 personality, whilst performance-based adaptability was related to cognitive abilities.     

Inherited and Environmental Moderators of Mother-Child Behavioral Contingency and Contingent Negativity at 27 Months

Previous research has shown that as depressive symptomology increases, mothers tend to show withdrawn or harsh interaction patterns with their children, and the quality of these interaction patterns have subsequently been linked to child behavior problems. However, little research has examined bidirectional influences between mothers and their children, and how these moment-to-moment contingencies differ based on heritable and environmental characteristics. We used data from the Early Growth and Development Study a prospective adoption study to examine how adoptive mothers’ depressive symptoms and children’s heritable tendencies for negative affectivity interact to predict the quality of mother-child interactions at child age 27-months. Results detected two distinct dyadic interaction patterns. The first was a withdrawn interaction style and was observed in children with a high heritable tendency for negative affect. In that style, mother and child interactions were not contingent upon each other, suggesting a lack of joint engagement. The second was a volatile interaction style, observed in children with a low heritable tendency for negative affect. In these cases, mother and child interactions were highly contingent but negative. Our findings demonstrate essential differences in how dyadic interaction patterns vary according to level of depressive symptomology and heritable tendency for negative affect.     

EARSHOT: A Minimal Neural Network Model of Incremental Human Speech Recognition

Despite the lack of invariance problem (the many-to-many mapping between acoustics and percepts), human listeners experience phonetic constancy and typically perceive what a speaker intends. Most models of human speech recognition (HSR) have side-stepped this problem, working with abstract, idealized inputs and deferring the challenge of working with real speech. In contrast, carefully engineered deep learning networks allow robust, real-world automatic speech recognition (ASR). However, the complexities of deep learning architectures and training regimens make it difficult to use them to provide direct insights into mechanisms that may support HSR. In this brief article, we report preliminary results from a two-layer network that borrows one element from ASR, long short-term memory nodes, which provide dynamic memory for a range of temporal spans. This allows the model to learn to map real speech from multiple talkers to semantic targets with high accuracy, with human-like timecourse of lexical access and phonological competition. Internal representations emerge that resemble phonetically organized responses in human superior temporal gyrus, suggesting that the model develops a distributed phonological code despite no explicit training on phonetic or phonemic targets. The ability to work with real speech is a major advance for cognitive models of HSR.     

How Experts Adapt Their Gaze Behavior When Modeling a Task to Novices

Domain experts regularly teach novice students how to perform a task. This often requires them to adjust their behavior to the less knowledgeable audience and, hence, to behave in a more didactic manner. Eye movement modeling examples (EMMEs) are a contemporary educational tool for displaying experts’ (natural or didactic) problem-solving behavior as well as their eye movements to learners. While research on expert-novice communication mainly focused on experts’ changes in explicit, verbal communication behavior, it is as yet unclear whether and how exactly experts adjust their nonverbal behavior. This study first investigated whether and how experts change their eye movements and mouse clicks (that are displayed in EMMEs) when they perform a task naturally versus teach a task didactically. Programming experts and novices initially debugged short computer codes in a natural manner. We first characterized experts’ natural problem-solving behavior by contrasting it with that of novices. Then, we explored the changes in experts’ behavior when being subsequently instructed to model their task solution didactically. Experts became more similar to novices on measures associated with experts’ automatized processes (i.e., shorter fixation durations, fewer transitions between code and output per click on the run button when behaving didactically). This adaptation might make it easier for novices to follow or imitate the expert behavior. In contrast, experts became less similar to novices for measures associated with more strategic behavior (i.e., code reading linearity, clicks on run button) when behaving didactically.     

Examples,practice problems,or both? Effects on motivation and learning in shorter and longer sequences

Research suggests some sequences of examples and problems (i.e., EE, EP) are more effective (higher test performance) and efficient (attained with equal/less mental effort) than others (PP, sometimes also PE). Recent findings suggest this is due to motivational variables (i.e., self-efficacy), but did not test this during the training phase. Moreover, prior research used only short task sequences. Therefore, we investigated effects on motivational variables, effectiveness, and efficiency in a short (Experiment 1; four learning tasks; n = 157) and longer task sequence (Experiment 2; eight learning tasks; n = 105). With short sequences, all example conditions were more effective, efficient, and motivating than PP. With longer sequences, all example conditions were more motivating and efficient than PP, but only EE was more effective than PP. Moreover, EE was most efficient during training, regardless of sequence length. These results suggest that example study (only) is more effective, efficient, and more motivating than PP.     

Learning through observing: Effects of modeling truth‐ and lie‐telling on children’s honesty

The current study examined the influence of observing another's lie‐ or truth‐telling – and its consequences – on children's own honesty about a transgression. Children (N = 224, 5–8 years of age) observed an experimenter (E) tell the truth or lie about a minor transgression in one of five conditions: (a) Truth‐Positive Outcome – E told the truth with a positive outcome; (b) Truth‐Negative Outcome – E told the truth with a negative outcome; (c) Lie‐Positive Outcome – E lied with a positive outcome; (d) Lie‐Negative Outcome – E lied with a negative outcome; (e) Control – E did not tell a lie or tell the truth. Later, to examine children's truth‐ or lie‐telling behavior, children participated in a temptation resistance paradigm where they were told not to peek at a trivia question answer. They either peeked or not, and subsequently lied or told the truth about that behavior. Additionally, children were asked to give moral evaluations of different truth‐ and lie‐telling vignettes. Overall, 85% of children lied. Children were less likely to lie about their own transgression in the TRP when they had previously witnessed the experimenter tell the truth with a positive outcome or tell a lie with a negative outcome.     

Using the UTAUT2 model to explain public acceptance of conditionally automated (L3) cars: A questionnaire study among 9,118 car drivers from eight European countries

We investigated public acceptance of conditionally automated (SAE Level 3) passenger cars using a questionnaire study among 9,118 car-drivers in eight European countries, as part of the European L3Pilot project. 71.06% of respondents considered conditionally automated cars easy to use while 28.03% of respondents planned to buy a conditionally automated car once it is available. 41.85% of respondents would like to use the time in the conditionally automated car for secondary activities. Among these 41.85%, respondents plan to talk to fellow travellers (44.76%), surf the internet, watch videos or TV shows (44%), observe the landscape (41.70%), and work (17.06%). The UTAUT2 (Unified Theory of Acceptance and Use of Technology) was applied to investigate the effects of performance and effort expectancy, social influence, facilitating conditions, and hedonic motivation on the behavioural intention to use conditionally automated cars. Structural equation analysis revealed that the UTAUT2 can be applied to conditional automation, with hedonic motivation, social influence, and performance expectancy influencing the behavioural intention to buy and use a conditionally automated car. The present study also found positive effects of facilitating conditions on effort expectancy and hedonic motivation. Social influence was a positive predictor of hedonic motivation, facilitating conditions, and performance expectancy. Age, gender and experience with advanced driver assistance systems had significant, yet small (<0.10), effects on behavioural intention. The implications of these results on the policy and best practices to enable large-scale implementation of conditionally automated cars on public roads are discussed.